Here, we focus on a language modeling approach [1]}, [2]}.
Although recent efforts in NMT tend to output fluent sentences, it is difficult to reflect the linguistic properties of the target language, as only the source information is taken into consideration when performing translation [3]}.
Additionally, language models are useful in that they contain target information that results in fluent output and can make predictions even if they do not know the source sentence.
In previous works utilizing a language model for NMT, both the language model and the conventional translation model have been prepared, wherein the final translation is performed by weighting both models.
In the Shallow Fusion mechanism [1]}, the output of the translation and language models are weighted at a fixed ratio.
In the Cold Fusion mechanism [5]}, a gate function is created to dynamically determine the weight of the language model considering the translation model.
In the Simple Fusion mechanism [2]}, outputs of both models are treated equally, whereas normalization steps vary.
